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The Lab · Leaderboards
2025-26last seasonAs of Sun, Sep 20

Whole-league leaderboards

Rank every qualified skater on any of 30 metrics — the on-off impact, puck-security, and finishing-luck numbers most sites never surface. Pick a metric, pick a pool, and read the whole league sorted the model's way. Percentiles are always position-relative.

On-Ice xG%

n = 201 qualified D

Share of on-ice expected goals that go the team's way at 5-on-5 while this player is out there.

5on5 · higher is better — leaders on top · %

#TmPlayerValuevs. position
1OTT60.1%
ELITE
100th
2COL59.7%
ELITE
99th
3CAR58.3%
ELITE
99th
4NYR58.0%
ELITE
98th
5COL57.9%
ELITE
98th
6EDM57.8%
ELITE
97th
7EDM57.6%
ELITE
97th
8CAR57.4%
ELITE
97th
9CAR57.2%
ELITE
96th
10COL57.0%
ELITE
96th
11COL56.9%
ELITE
95th
12CAR56.6%
ELITE
95th
13TBL56.5%
ELITE
94th
14COL56.4%
ELITE
94th
15OTT56.4%
ELITE
93rd
16UTA55.8%
ELITE
93rd
17TBL55.6%
ELITE
92nd
18CBJ55.4%
ELITE
92nd
19OTT55.0%
ELITE
91st
20VGK55.0%
ELITE
91st
21VGK54.8%
ELITE
90th
22CBJ54.8%
ELITE
90th
23DET54.7%
STRONG
89th
24VGK54.7%
STRONG
88th
25OTT54.7%
STRONG
88th
26TBL54.7%
STRONG
87th
27CAR54.5%
STRONG
87th
28VGK54.3%
STRONG
86th
29UTA54.3%
STRONG
86th
30COL54.2%
STRONG
86th
31FLA54.1%
STRONG
85th
32MTL54.0%
STRONG
85th
33STL53.9%
STRONG
84th
34LAK53.7%
STRONG
84th
35DAL53.7%
STRONG
83rd
36OTT53.6%
STRONG
82nd
37TBL53.5%
STRONG
82nd
38UTA53.4%
STRONG
81st
39BUF53.3%
STRONG
81st
40OTT53.3%
STRONG
80th
41PHI53.1%
STRONG
80th
42UTA53.1%
STRONG
79th
43VAN53.0%
STRONG
79th
44WSH52.9%
STRONG
79th
45VGK52.9%
STRONG
78th
46DAL52.9%
STRONG
77th
47TBL52.8%
STRONG
77th
48DET52.6%
STRONG
76th
49FLA52.6%
STRONG
76th
50LAK52.5%
STRONG
75th
51VGK52.5%
STRONG
75th
52PIT52.4%
STRONG
74th
53PHI52.1%
STRONG
74th
54STL52.1%
STRONG
74th
55UTA52.1%
STRONG
72nd
56ANA52.1%
STRONG
72nd
57PIT52.0%
STRONG
72nd
58CBJ52.0%
STRONG
71st
59OTT51.9%
STRONG
71st
60CAR51.9%
STRONG
70th
61MIN51.8%
STRONG
70th
62NSH51.7%
AVERAGE
69th
63PHI51.7%
AVERAGE
69th
64PHI51.5%
AVERAGE
68th
65NYR51.4%
AVERAGE
68th
66MIN51.4%
AVERAGE
67th
67LAK51.3%
AVERAGE
67th
68MIN51.3%
AVERAGE
66th
69FLA51.3%
AVERAGE
66th
70WSH51.3%
AVERAGE
66th
71PIT51.2%
AVERAGE
65th
72UTA51.2%
AVERAGE
65th
73STL51.2%
AVERAGE
64th
74ANA51.1%
AVERAGE
64th
75PHI51.1%
AVERAGE
63rd
76PHI51.1%
AVERAGE
63rd
77MTL51.1%
AVERAGE
62nd
78LAK51.1%
AVERAGE
61st
79BUF51.0%
AVERAGE
61st
80PIT51.0%
AVERAGE
61st
81WPG50.9%
AVERAGE
60th
82NYI50.9%
AVERAGE
59th
83CBJ50.8%
AVERAGE
59th
84WSH50.8%
AVERAGE
59th
85CBJ50.8%
AVERAGE
58th
86NJD50.8%
AVERAGE
58th
87TBL50.6%
AVERAGE
57th
88BUF50.5%
AVERAGE
56th
89ANA50.5%
AVERAGE
56th
90WPG50.5%
AVERAGE
55th
91FLA50.5%
AVERAGE
55th
92SJS50.4%
AVERAGE
54th
93DAL50.3%
AVERAGE
54th
94BUF50.3%
AVERAGE
53rd
95SEA50.2%
AVERAGE
53rd
96ANA50.2%
AVERAGE
52nd
97BOS50.2%
AVERAGE
52nd
98UTA50.2%
AVERAGE
52nd
99BUF50.1%
AVERAGE
51st
100ANA50.0%
AVERAGE
51st

◆ Method · Single-metric leaderboard over players with games_played >= minGP in the metric's situation; percentiles are position-group relative. Data: MoneyPuck data/mp_skaters_2025.csv. · percentiles are position-relative, model-estimated proxies from MoneyPuck aggregates via the analytics sidecar.